Motivation of the Project
Engagement, Ohio, USA is a small town with huge potential and experiencing sudden growth. We aim to analyse the data and derive insights which will help to plan the budget utilization wisely and to develop the infrastructure of the town to keep up with the growth.
For our Visual Analytics Project we aim to tackle the task 3 of the Vast Challenge.
The Problems
Challenge 3:
Economic considers the financial health of the city. Over time, are businesses growing or shrinking? How are people changing jobs? Are standards of living improving or declining over time?
Consider the financial status of Engagement’s businesses and residents, and use visual analytic techniques to address these questions.
Problem 1: * Over the period covered by the dataset, which businesses appear to be more prosperous? Which appear to be struggling? Describe your rationale for your answers. Limit your response to 10 images and 500 words.
How does the financial health of the residents change over the period covered by the dataset? How do wages compare to the overall cost of living in Engagement? Are there groups that appear to exhibit similar patterns? Describe your rationale for your answers. Limit your response to 10 images and 500 words.
Describe the health of the various employers within the city limits. What employment patterns do you observe? Do you notice any areas of particularly high or low turnover? Limit your response to 10 images and 500 words.
Loading the required packages
packages = c('tidyverse','ggdist','gghalves','ggthemes','hrbrthemes','ggridges','patchwork','zoo', 'ggrepel','ggiraph','lubridate','gganimate','scales')
for(p in packages){
if(!require(p, character.only = T)){
install.packages(p)
}
library(p, character.only = T)
}
Our Solution
Problem 1: (Shachi) Over the period covered by the dataset, which businesses appear to be more prosperous? Which appear to be struggling? Describe your rationale for your answers. Limit your response to 10 images and 500 words.
Problem 2: (Rakendu) How does the financial health of the residents change over the period covered by the dataset? How do wages compare to the overall cost of living in Engagement? Are there groups that appear to exhibit similar patterns? Describe your rationale for your answers. Limit your response to 10 images and 500 words.
Dataset
The Financial Journal of the participants was used to derive insights about the financial health of the residents. Let us take a look at the data :
Data Wrangling
We use the dplyr package to group by participant id and date from timestamp to find the income and expenditure of the participants. The code can be seen here
We will read in the wrangled data saved as rds file in order reduce the size
participant_fin <- read_rds("data/rds/participant_fin.rds")
We can use a scatterplot to understand the variations in income vs expenses of participants over time.
participant_fin %>%
filter(date >= 'Apr 2022') %>%
transform(date = as.Date(date, frac = 1)) %>%
ggplot(aes(x=income, y = abs(expense), size = savings, color = educationLevel))+
geom_point(alpha=0.7) +
ggtitle("Income vs Expense by different Education Levels") +
ylab("Expense") +
xlab("Income")+
theme_minimal() +
theme(axis.line = element_line(size = 0.5),
axis.text = element_text(size = 16),
axis.title = element_text(size=16),
axis.title.y = element_text(angle = 0),
legend.title = element_text(size =16),
legend.text = element_text(size = 16),
plot.title = element_text(size =20,hjust = 0.5))+
labs(title ='Period : {frame_time}')+
transition_time(date)+
ease_aes('linear')
